Using Bi-Seasonal WorldView-2 Multi-Spectral Data and Supervised Random Forest Classification to Map Coastal Plant Communities in Everglades National Park

Abstract

Coastal plant communities are being transformed or lost because of sea level rise (SLR) and land-use change. In conjunction with SLR, the Florida Everglades ecosystem has undergone large-scale drainage and restoration, altering coastal vegetation throughout south Florida. To understand how coastal plant communities are changing over time, accurate mapping techniques are needed that can define plant communities at a fine-enough resolution to detect fine-scale changes. We explored using bi-seasonal versus single-season WorldView-2 satellite data to map three mangrove and four adjacent plant communities, including the buttonwood/glycophyte community that harbors the federally-endangered plant Chromolaena frustrata. Bi-seasonal data were more effective than single-season to differentiate all communities of interest. Bi-seasonal data combined with Light Detection and Ranging (LiDAR) elevation data were used to map coastal plant communities of a coastal stretch within Everglades National Park (ENP). Overall map accuracy was 86%. Black and red mangroves were the dominant communities and covered 50% of the study site. All the remaining communities had ≤10% cover, including the buttonwood/glycophyte community. ENP harbors 21 rare coastal species threatened by SLR. The spatially explicit, quantitative data provided by our map provides a fine-scale baseline for monitoring future change in these species’ habitats. Our results also offer a method to monitor vegetation change in other threatened habitats.

An Ultra-Resolution Features Extraction Suite for Community-Level Vegetation Differentiation and Mapping at a Sub-Meter Resolution

“…In a similar way, the addition of summer images to the previously used autumn-season images provided 12.8% higher accuracy for the ultra-resolution suite and 13.2% higher accuracy for the very high-resolution suite in this research across three cool temperate ecosystem sites. Wendelberger et al [109] also explored bi-seasonal versus single-season WorldView-2 images to map three mangrove and four adjacent plant communities and found that bi-seasonal images were more effective than single-season to differentiate all communities of interest, in line with this research carried out in three mountainous ecosystems. Ferreira et al [37] utilized WorldView-3 images acquired in the dry and wet seasons and emphasized the usage of seasonal images for tree species discrimination in semi-deciduous forests.…”

Remote sensing of savanna woody species diversity: A systematic review of data types and assessment methods

“…They reported overall accuracies of 83% (Landsat-8) and 86% (SPOT-6). [ 73 ] used RF and Worldview-2 for a bi-seasonal analysis of seven woody plants. The authors recorded woody plant species from 4-m 2 plots in a Subtropical savanna forest covering 71 km 2 and reported overall accuracy of 86%.…”

Remote Sensing Applications for Monitoring Terrestrial Protected Areas: Progress in the Last Decade

“…Herrero et al [35] found out that the RF classifier was the best method for distinguishing African savannas in Chobe National Park in Botswana, which has a highly heterogeneous mixture of woody and herbaceous vegetation. Wendelberger et al [53] observed that bi-seasonal data were more effective than single-season data to differentiate coastal plant communities in Everglades National Park.…”

“…A major challenge is how to provide valuable insights into site-specific PA management through the interpretation of remote sensing images rather than just describe the symptoms [42]. Remote sensing has the potential to identify the complex drivers of change in human-nature coupled systems, which is generally induced by human activities (including human settlements [66], urbanization [31], excessive tourism [35], mining [85], logging [67], agriculture expansion [37], grazing [127], and hunting [124]) and natural disturbances (including climate variabilities such as droughts [101] and sea-level rise [53], as well as species invasions [118] and megafauna roaming [61,98]). The analysis of remote sensing data alone has limits for discovering accurate and reliable information on the driving forces that threaten ecosystems and for proposing countermeasures for PAs.…”